{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Simple Image Processing App with MONAI Deploy App SDK\n",
    "\n",
    "This tutorial shows how a simple image processing application can be created with MONAI Deploy App SDK.\n",
    "\n",
    "\n",
    "## Creating Operators and connecting them in Application class\n",
    "\n",
    "We will implement an application that consists of three Operators:\n",
    "\n",
    "- **SobelOperator**: Apply a Sobel edge detector.\n",
    "    - **Input**: a file path (`Path`)\n",
    "    - **Output**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "- **MedianOperator**: Apply a Median filter for noise reduction.\n",
    "    - **Input**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "    - **Output**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "- **GaussianOperator**: Apply a Gaussian filter for smoothening.\n",
    "    - **Input**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "    - **Output**: a file path (`Path`)\n",
    "\n",
    "The workflow of the application would look like this.\n",
    "\n",
    "```{mermaid}\n",
    "%%{init: {\"theme\": \"base\", \"themeVariables\": { \"fontSize\": \"16px\"}} }%%\n",
    "\n",
    "classDiagram\n",
    "    direction LR\n",
    "\n",
    "    SobelOperator --|> MedianOperator : image...image\n",
    "    MedianOperator --|> GaussianOperator : image...image\n",
    "\n",
    "    class SobelOperator {\n",
    "        <in>image : Path\n",
    "        image(out) : IN_MEMORY\n",
    "    }\n",
    "    class MedianOperator {\n",
    "        <in>image : IN_MEMORY\n",
    "        image(out) : IN_MEMORY\n",
    "    }\n",
    "    class GaussianOperator {\n",
    "        <in>image : IN_MEMORY\n",
    "        image(out) : Path\n",
    "    }\n",
    "```\n",
    "\n",
    "### Setup environment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install necessary image loading/processing packages for the application\n",
    "!python -c \"import PIL\" || pip install -q \"Pillow\"\n",
    "!python -c \"import skimage\" || pip install -q \"scikit-image\"\n",
    "!python -c \"import matplotlib\" || pip install -q \"matplotlib\"\n",
    "%matplotlib inline\n",
    "\n",
    "# Install MONAI Deploy App SDK package\n",
    "!python -c \"import monai.deploy\" || pip install -q \"monai-deploy-app-sdk\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download test input\n",
    "\n",
    "We will use a test input from the following.\n",
    "\n",
    "> Case courtesy of Dr Bruno Di Muzio, <a href=\"https://radiopaedia.org/?lang=us\">Radiopaedia.org</a>. From the case <a href=\"https://radiopaedia.org/cases/41113?lang=us\">rID: 41113</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test input file path: '/tmp/simple_app/normal-brain-mri-4.png'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_895166/2727006292.py:16: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n",
      "  io.imshow(test_image)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7e9183c539d0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_input_folder = \"/tmp/simple_app\"\n",
    "test_input_path = test_input_folder + \"/normal-brain-mri-4.png\"\n",
    "\n",
    "!python -c \"import wget\" || pip install -q \"wget\"\n",
    "!mkdir -p {test_input_folder}\n",
    "\n",
    "from skimage import io\n",
    "import wget\n",
    "\n",
    "\n",
    "wget.download(\"https://user-images.githubusercontent.com/1928522/133383228-2357d62d-316c-46ad-af8a-359b56f25c87.png\", test_input_path)\n",
    "\n",
    "print(f\"Test input file path: {test_input_path!r}\")\n",
    "\n",
    "test_image = io.imread(test_input_path)\n",
    "io.imshow(test_image)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up environment variables\n",
    "The application uses well-known environment variables for the input/output data path, working dir, as well as AI model file path if applicable. Defaults are used if these environment variable are absent.\n",
    "\n",
    "In this example, only the input data path and output path need to be set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: HOLOSCAN_INPUT_FOLDER=/tmp/simple_app\n",
      "env: HOLOSCAN_INPUT_PATH=/tmp/simple_app/normal-brain-mri-4.png\n",
      "env: HOLOSCAN_OUTPUT_PATH=output\n",
      "/tmp/simple_app/normal-brain-mri-4.png\n"
     ]
    }
   ],
   "source": [
    "output_path = \"output\"\n",
    "%env HOLOSCAN_INPUT_FOLDER {test_input_folder}\n",
    "%env HOLOSCAN_INPUT_PATH {test_input_path}\n",
    "%env HOLOSCAN_OUTPUT_PATH {output_path}\n",
    "%ls $HOLOSCAN_INPUT_PATH"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup imports\n",
    "\n",
    "Let's import necessary classes/decorators to define the application and operators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from monai.deploy.conditions import CountCondition\n",
    "from monai.deploy.core import AppContext, Application, ConditionType, Fragment, Operator, OperatorSpec"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Operator classes\n",
    "\n",
    "Each Operator class inherits from the [Operator](/modules/_autosummary/monai.deploy.core.Operator) class, with the input and output ports of the operator specified using the `setup` method. Business logic would be implemented in the <a href=\"../../modules/_autosummary/monai.deploy.core.Operator.html#monai.deploy.core.Operator.compute\">compute</a> method.\n",
    "\n",
    ":::{note}\n",
    "- the way to specify operator input and output in this version of the App SDK is different from versions, up to and including V0.5, where Python decorators are used. Decorator support will be re-introduced in future releases\n",
    "- the first operator(SobelOperator)'s input and the last operator(GaussianOperator)'s output are data paths, which are not data types supported by operator ports but as object can be used as optional input and output. In the example, these paths are passed in as arguments to the constructor and the operator classes have defined logic on using the paths, e.g. reading from or writing to the path. The application class is responsible for setting the path by parsing the well-known environment variables\n",
    ":::\n",
    "\n",
    "#### SobelOperator\n",
    "\n",
    "SobelOperator is the first operator (the root operator in the workflow graph). It reads from the input file/folder path, which is passed in as an argument on the constructor and assigned to an attribute.\n",
    "\n",
    "Once loaded and processed, the image data (as a Numpy array) is set to the output (<a href=\"../../modules/_autosummary/monai.deploy.core.OutputContext.html#monai.deploy.core.OutputContext.emit\">op_output.emit(value, label)</a>)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SobelOperator(Operator):\n",
    "    \"\"\"This Operator implements a Sobel edge detector.\n",
    "\n",
    "    It has the following input and output:\n",
    "        single input:\n",
    "          a image file, first one found in the input folder\n",
    "        single output:\n",
    "          array object in memory\n",
    "    \"\"\"\n",
    "\n",
    "    DEFAULT_INPUT_FOLDER = Path.cwd() / \"input\"\n",
    "\n",
    "    def __init__(self, fragment: Fragment, *args, input_path: Path, **kwargs):\n",
    "        \"\"\"Create an instance to be part of the given application (fragment).\n",
    "\n",
    "        Args:\n",
    "            fragment (Fragment): An instance of the Application class which is derived from Fragment\n",
    "            input_path (Path): The path of the input image file or folder containing the image file\n",
    "        \"\"\"\n",
    "        self.index = 0\n",
    "\n",
    "        # May want to validate the path, but it should really be validated when the compute function is called, also,\n",
    "        # when file path as input is supported in the operator or execution context, input_folder needs not an attribute.\n",
    "        self.input_path = (\n",
    "            input_path if input_path else SobelOperator.DEFAULT_INPUT_FOLDER\n",
    "        )\n",
    "\n",
    "        # Need to call the base class constructor last\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.output(\"out1\")\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        from skimage import filters, io\n",
    "\n",
    "        self.index += 1\n",
    "        print(f\"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}\")\n",
    "\n",
    "        # Ideally the op_input or execution context should provide the file path\n",
    "        # to read data from, for operators that are file input based.\n",
    "        # For now, use a temporary way to get input path. e.g. value set on init\n",
    "        input_path = self.input_path\n",
    "        print(f\"Input from: {input_path}, whose absolute path: {input_path.absolute()}\")\n",
    "        if input_path.is_dir():\n",
    "            input_path = next(input_path.glob(\"*.*\"))  # take the first file\n",
    "\n",
    "        data_in = io.imread(input_path)[:, :, :3]  # discard alpha channel if exists\n",
    "        data_out = filters.sobel(data_in)\n",
    "\n",
    "        op_output.emit(data_out, \"out1\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MedianOperator\n",
    "\n",
    "MedianOperator is a middle operator that accepts data from SobelOperator and passes the processed image data to GaussianOperator.\n",
    "\n",
    "Its input data type is image in Numpy array. Once received at the input (`op_input.receive(label)`), the image is transformed and set to the output (`op_output.emit(value, label)`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MedianOperator(Operator):\n",
    "    \"\"\"This Operator implements a noise reduction.\n",
    "\n",
    "    The algorithm is based on the median operator.\n",
    "    It ingests a single input and provides a single output, both are in-memory image arrays\n",
    "    \"\"\"\n",
    "\n",
    "    # Define __init__ method with super().__init__() if you want to override the default behavior.\n",
    "    def __init__(self, fragment: Fragment, *args, **kwargs):\n",
    "        \"\"\"Create an instance to be part of the given application (fragment).\n",
    "\n",
    "        Args:\n",
    "            fragment (Fragment): The instance of Application class which is derived from Fragment\n",
    "        \"\"\"\n",
    "\n",
    "        self.index = 0\n",
    "\n",
    "        # Need to call the base class constructor last\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.input(\"in1\")\n",
    "        spec.output(\"out1\")\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        from skimage.filters import median\n",
    "\n",
    "        self.index += 1\n",
    "        print(f\"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}\")\n",
    "        data_in = op_input.receive(\"in1\")\n",
    "        data_out = median(data_in)\n",
    "        op_output.emit(data_out, \"out1\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GaussianOperator\n",
    "\n",
    "GaussianOperator is the last operator (a leaf operator in the workflow graph) and saves the processed image to a file, whose path is provided via an argument on the constructor.\n",
    "\n",
    "This operator can also output the image in Numpy array in memory without requiring a receiver for it. This can be set up by using the optional output condition in the function `setup`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GaussianOperator(Operator):\n",
    "    \"\"\"This Operator implements a smoothening based on Gaussian.\n",
    "\n",
    "    It has the following input and output:\n",
    "        single input:\n",
    "          an image array object\n",
    "        single output:\n",
    "          an image array object, without enforcing a downstream receiver\n",
    "\n",
    "    Besides, this operator also saves the image file in the given output folder.\n",
    "    \"\"\"\n",
    "\n",
    "    DEFAULT_OUTPUT_FOLDER = Path.cwd() / \"output\"\n",
    "\n",
    "    def __init__(self, fragment: Fragment, *args, output_folder: Path, **kwargs):\n",
    "        \"\"\"Create an instance to be part of the given application (fragment).\n",
    "\n",
    "        Args:\n",
    "            fragment (Fragment): The instance of Application class which is derived from Fragment\n",
    "            output_folder (Path): The folder to save the output file.\n",
    "        \"\"\"\n",
    "        self.output_folder = output_folder if output_folder else GaussianOperator.DEFAULT_OUTPUT_FOLDER\n",
    "        self.index = 0\n",
    "\n",
    "        # If `self.sigma_default` is set here (e.g., `self.sigma_default = 0.2`), then\n",
    "        # the default value by `param()` in `setup()` will be ignored.\n",
    "        # (you can just call `spec.param(\"sigma_default\")` in `setup()` to use the\n",
    "        # default value)\n",
    "        self.sigma_default = 0.2\n",
    "        self.channel_axis = 2\n",
    "\n",
    "        # Need to call the base class constructor last\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.input(\"in1\")\n",
    "        spec.output(\"out1\").condition(ConditionType.NONE)  # Condition is for no or not-ready receiver ports.\n",
    "        spec.param(\"sigma_default\", 0.2)\n",
    "        spec.param(\"channel_axis\", 2)\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        from skimage.filters import gaussian\n",
    "        from skimage.io import imsave\n",
    "        import numpy as np\n",
    "\n",
    "        self.index += 1\n",
    "        print(f\"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}\")\n",
    "\n",
    "        data_in = op_input.receive(\"in1\")\n",
    "        data_out = gaussian(data_in, sigma=self.sigma_default, channel_axis=self.channel_axis)\n",
    "\n",
    "        # Make sure the data type is what PIL Image can support, as the imsave function calls PIL Image fromarray()\n",
    "        # Some details can be found at https://stackoverflow.com/questions/55319949/pil-typeerror-cannot-handle-this-data-type\n",
    "        print(f\"Data type of output: {type(data_out)!r}, max = {np.max(data_out)!r}\")\n",
    "        if np.max(data_out) <= 1:\n",
    "            data_out = (data_out*255).astype(np.uint8)\n",
    "        print(f\"Data type of output post conversion: {type(data_out)!r}, max = {np.max(data_out)!r}\")\n",
    "\n",
    "        # For now, use attribute of self to find the output path.\n",
    "        self.output_folder.mkdir(parents=True, exist_ok=True)\n",
    "        output_path = self.output_folder / \"final_output.png\"\n",
    "        imsave(output_path, data_out)\n",
    "\n",
    "        op_output.emit(data_out, \"out1\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Application class\n",
    "\n",
    "Our application class would look like below.\n",
    "\n",
    "It defines `App` class, inheriting [Application](/modules/_autosummary/monai.deploy.core.Application) class.\n",
    "\n",
    "In `compose()` method, objects of `SobelOperator`, `MedianOperator`, and `GaussianOperator` classes are created\n",
    "and connected through <a href=\"../../modules/_autosummary/monai.deploy.core.Application.html#monai.deploy.core.Application.add_flow\">self.add_flow()</a>.\n",
    "\n",
    "> add_flow(source_op, destination_op, io_map=None)\n",
    "\n",
    "`io_map` is a dictionary of mapping from the source operator's label to the destination operator's label(s) and its type is `Set[Tuple[str, str]]`.\n",
    "\n",
    "We can skip specifying `io_map` if both the number of `source_op`'s outputs and the number of `destination_op`'s inputs are one so `self.add_flow(sobel_op, median_op)` is same with `self.add_flow(sobel_op, median_op, {\"image\": \"image\"})` or `self.add_flow(sobel_op, median_op, {\"image\": {\"image\"}})`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statement, App().run(), is needed when this is run directly by the interpreter.\n"
     ]
    }
   ],
   "source": [
    "class App(Application):\n",
    "    \"\"\"This is a very basic application.\n",
    "\n",
    "    This showcases the MONAI Deploy application framework.\n",
    "    \"\"\"\n",
    "\n",
    "    # App's name. <class name>('App') if not specified.\n",
    "    name = \"simple_imaging_app\"\n",
    "    # App's description. <class docstring> if not specified.\n",
    "    description = \"This is a very simple application.\"\n",
    "    # App's version. <git version tag> or '0.0.0' if not specified.\n",
    "    version = \"0.1.0\"\n",
    "\n",
    "    def compose(self):\n",
    "        \"\"\"This application has three operators.\n",
    "\n",
    "        Each operator has a single input and a single output port.\n",
    "        Each operator performs some kind of image processing function.\n",
    "        \"\"\"\n",
    "        app_context = Application.init_app_context({})  # Do not pass argv in Jupyter notebook\n",
    "        sample_data_path = Path(app_context.input_path)\n",
    "        output_data_path = Path(app_context.output_path)\n",
    "        print(f\"sample_data_path: {sample_data_path}\")\n",
    "\n",
    "        # Please note that the Application object, self, is passed as the first positional argument\n",
    "        # and the others as kwargs.\n",
    "        # Also note the CountCondition of 1 on the first operator, indicating to the application executor\n",
    "        # to invoke this operator, hence the pipeline, only once.\n",
    "        sobel_op = SobelOperator(self, CountCondition(self, 1), input_path=sample_data_path, name=\"sobel_op\")\n",
    "        median_op = MedianOperator(self, name=\"median_op\")\n",
    "        gaussian_op = GaussianOperator(self, output_folder=output_data_path, name=\"gaussian_op\")\n",
    "        self.add_flow(\n",
    "            sobel_op,\n",
    "            median_op,\n",
    "            {\n",
    "                (\"out1\", \"in1\"),\n",
    "            },\n",
    "        )\n",
    "        self.add_flow(\n",
    "            median_op,\n",
    "            gaussian_op,\n",
    "            {\n",
    "                (\n",
    "                    \"out1\",\n",
    "                    \"in1\",\n",
    "                )\n",
    "            },\n",
    "        )  # Using port name is optional for single port cases\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    print(\"The statement, App().run(), is needed when this is run directly by the interpreter.\")\n",
    "    # App().run()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing app locally\n",
    "\n",
    "We can execute the app in the Jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[info] [fragment.cpp:705] Loading extensions from configs...\n",
      "[2025-04-22 12:03:07,039] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, triton_server_netloc=None, argv=[])\n",
      "[2025-04-22 12:03:07,047] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app/normal-brain-mri-4.png, output_path=output, model_path=models, workdir=), triton_server_netloc=\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample_data_path: /tmp/simple_app/normal-brain-mri-4.png\n",
      "Number of times operator sobel_op whose class is defined in __main__ called: 1\n",
      "Input from: /tmp/simple_app/normal-brain-mri-4.png, whose absolute path: /tmp/simple_app/normal-brain-mri-4.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[info] [gxf_executor.cpp:265] Creating context\n",
      "[info] [gxf_executor.cpp:2396] Activating Graph...\n",
      "[info] [gxf_executor.cpp:2426] Running Graph...\n",
      "[info] [gxf_executor.cpp:2428] Waiting for completion...\n",
      "[info] [greedy_scheduler.cpp:191] Scheduling 3 entities\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of times operator median_op whose class is defined in __main__ called: 1\n",
      "Number of times operator gaussian_op whose class is defined in __main__ called: 1\n",
      "Data type of output: <class 'numpy.ndarray'>, max = 0.35821119421406195\n",
      "Data type of output post conversion: <class 'numpy.ndarray'>, max = 91\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[info] [greedy_scheduler.cpp:372] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n",
      "[info] [greedy_scheduler.cpp:401] Scheduler finished.\n",
      "[info] [gxf_executor.cpp:2431] Deactivating Graph...\n",
      "[info] [gxf_executor.cpp:2439] Graph execution finished.\n",
      "[info] [gxf_executor.cpp:295] Destroying context\n"
     ]
    }
   ],
   "source": [
    "!rm -rf {output_path}\n",
    "App().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final_output.png\n"
     ]
    }
   ],
   "source": [
    "!ls {output_path}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_895166/1643627018.py:3: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n",
      "  io.imshow(output_image)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7e9183ca1c30>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_image_path = output_path + \"/final_output.png\"\n",
    "output_image = io.imread(output_image_path)\n",
    "io.imshow(output_image)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the application is verified inside Jupyter notebook, we can write the above Python code into Python files in an application folder.\n",
    "\n",
    "The application folder structure would look like below:\n",
    "\n",
    "```bash\n",
    "simple_imaging_app\n",
    "├── __main__.py\n",
    "├── app.py\n",
    "├── gaussian_operator.py\n",
    "├── median_operator.py\n",
    "└── sobel_operator.py\n",
    "```\n",
    "\n",
    ":::{note}\n",
    "We can create a single application Python file (such as `simple_imaging_app.py`) that includes the content of the files, instead of creating multiple files.\n",
    "You will see such example in <a href=\"./02_mednist_app.html#executing-app-locally\">MedNist Classifier Tutorial</a>.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an application folder\n",
    "!mkdir -p simple_imaging_app"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sobel_operator.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting simple_imaging_app/sobel_operator.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile simple_imaging_app/sobel_operator.py\n",
    "\n",
    "from pathlib import Path\n",
    "from monai.deploy.core import Fragment, Operator, OperatorSpec\n",
    "\n",
    "class SobelOperator(Operator):\n",
    "    \"\"\"This Operator implements a Sobel edge detector.\n",
    "\n",
    "    It has the following input and output:\n",
    "        single input:\n",
    "          a image file, first one found in the input folder\n",
    "        single output:\n",
    "          array object in memory\n",
    "    \"\"\"\n",
    "\n",
    "    DEFAULT_INPUT_FOLDER = Path.cwd() / \"input\"\n",
    "\n",
    "    def __init__(self, fragment: Fragment, *args, input_path: Path, **kwargs):\n",
    "        \"\"\"Create an instance to be part of the given application (fragment).\n",
    "\n",
    "        Args:\n",
    "            fragment (Fragment): An instance of the Application class which is derived from Fragment\n",
    "            input_path (Path): The path of the input image file or folder containing the image file\n",
    "        \"\"\"\n",
    "        self.index = 0\n",
    "\n",
    "        # May want to validate the path, but it should really be validated when the compute function is called, also,\n",
    "        # when file path as input is supported in the operator or execution context, input_folder needs not an attribute.\n",
    "        self.input_path = (\n",
    "            input_path if input_path else SobelOperator.DEFAULT_INPUT_FOLDER\n",
    "        )\n",
    "\n",
    "        # Need to call the base class constructor last\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.output(\"out1\")\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        from skimage import filters, io\n",
    "\n",
    "        self.index += 1\n",
    "        print(f\"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}\")\n",
    "\n",
    "        # Ideally the op_input or execution context should provide the file path\n",
    "        # to read data from, for operators that are file input based.\n",
    "        # For now, use a temporary way to get input path. e.g. value set on init\n",
    "        input_path = self.input_path\n",
    "        print(f\"Input from: {input_path}, whose absolute path: {input_path.absolute()}\")\n",
    "        if input_path.is_dir():\n",
    "            input_path = next(input_path.glob(\"*.*\"))  # take the first file\n",
    "\n",
    "        data_in = io.imread(input_path)[:, :, :3]  # discard alpha channel if exists\n",
    "        data_out = filters.sobel(data_in)\n",
    "\n",
    "        op_output.emit(data_out, \"out1\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### median_operator.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting simple_imaging_app/median_operator.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile simple_imaging_app/median_operator.py\n",
    "from monai.deploy.core import Fragment, Operator, OperatorSpec\n",
    "\n",
    "\n",
    "# Decorator support is not available in this version of the SDK, to be re-introduced later\n",
    "# @md.env(pip_packages=[\"scikit-image >= 0.17.2\"])\n",
    "class MedianOperator(Operator):\n",
    "    \"\"\"This Operator implements a noise reduction.\n",
    "\n",
    "    The algorithm is based on the median operator.\n",
    "    It ingests a single input and provides a single output, both are in-memory image arrays\n",
    "    \"\"\"\n",
    "\n",
    "    # Define __init__ method with super().__init__() if you want to override the default behavior.\n",
    "    def __init__(self, fragment: Fragment, *args, **kwargs):\n",
    "        \"\"\"Create an instance to be part of the given application (fragment).\n",
    "\n",
    "        Args:\n",
    "            fragment (Fragment): The instance of Application class which is derived from Fragment\n",
    "        \"\"\"\n",
    "\n",
    "        self.index = 0\n",
    "\n",
    "        # Need to call the base class constructor last\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.input(\"in1\")\n",
    "        spec.output(\"out1\")\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        from skimage.filters import median\n",
    "\n",
    "        self.index += 1\n",
    "        print(f\"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}\")\n",
    "        data_in = op_input.receive(\"in1\")\n",
    "        data_out = median(data_in)\n",
    "        op_output.emit(data_out, \"out1\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gaussian_operator.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting simple_imaging_app/gaussian_operator.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile simple_imaging_app/gaussian_operator.py\n",
    "from pathlib import Path\n",
    "\n",
    "from monai.deploy.core import ConditionType, Fragment, Operator, OperatorSpec\n",
    "\n",
    "\n",
    "# Decorator support is not available in this version of the SDK, to be re-introduced later\n",
    "# @md.env(pip_packages=[\"scikit-image >= 0.17.2\"])\n",
    "class GaussianOperator(Operator):\n",
    "    \"\"\"This Operator implements a smoothening based on Gaussian.\n",
    "\n",
    "    It has the following input and output:\n",
    "        single input:\n",
    "          an image array object\n",
    "        single output:\n",
    "          an image array object, without enforcing a downstream receiver\n",
    "\n",
    "    Besides, this operator also saves the image file in the given output folder.\n",
    "    \"\"\"\n",
    "\n",
    "    DEFAULT_OUTPUT_FOLDER = Path.cwd() / \"output\"\n",
    "\n",
    "    def __init__(self, fragment: Fragment, *args, output_folder: Path, **kwargs):\n",
    "        \"\"\"Create an instance to be part of the given application (fragment).\n",
    "\n",
    "        Args:\n",
    "            fragment (Fragment): The instance of Application class which is derived from Fragment\n",
    "            output_folder (Path): The folder to save the output file.\n",
    "        \"\"\"\n",
    "        self.output_folder = output_folder if output_folder else GaussianOperator.DEFAULT_OUTPUT_FOLDER\n",
    "        self.index = 0\n",
    "\n",
    "        # If `self.sigma_default` is set here (e.g., `self.sigma_default = 0.2`), then\n",
    "        # the default value by `param()` in `setup()` will be ignored.\n",
    "        # (you can just call `spec.param(\"sigma_default\")` in `setup()` to use the\n",
    "        # default value)\n",
    "        self.sigma_default = 0.2\n",
    "        self.channel_axis = 2\n",
    "\n",
    "        # Need to call the base class constructor last\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.input(\"in1\")\n",
    "        spec.output(\"out1\").condition(ConditionType.NONE)  # Condition is for no or not-ready receiver ports.\n",
    "        spec.param(\"sigma_default\", 0.2)\n",
    "        spec.param(\"channel_axis\", 2)\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        from skimage.filters import gaussian\n",
    "        from skimage.io import imsave\n",
    "        import numpy as np\n",
    "\n",
    "        self.index += 1\n",
    "        print(f\"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}\")\n",
    "\n",
    "        data_in = op_input.receive(\"in1\")\n",
    "        data_out = gaussian(data_in, sigma=self.sigma_default, channel_axis=self.channel_axis)\n",
    "\n",
    "        # Make sure the data type is what PIL Image can support, as the imsave function calls PIL Image fromarray()\n",
    "        # Some details can be found at https://stackoverflow.com/questions/55319949/pil-typeerror-cannot-handle-this-data-type\n",
    "        print(f\"Data type of output: {type(data_out)!r}, max = {np.max(data_out)!r}\")\n",
    "        if np.max(data_out) <= 1:\n",
    "            data_out = (data_out * 255).astype(np.uint8)\n",
    "        print(f\"Data type of output post conversion: {type(data_out)!r}, max = {np.max(data_out)!r}\")\n",
    "\n",
    "        # For now, use attribute of self to find the output path.\n",
    "        self.output_folder.mkdir(parents=True, exist_ok=True)\n",
    "        output_path = self.output_folder / \"final_output.png\"\n",
    "        imsave(output_path, data_out)\n",
    "\n",
    "        op_output.emit(data_out, \"out1\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### app.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting simple_imaging_app/app.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile simple_imaging_app/app.py\n",
    "import logging\n",
    "from pathlib import Path\n",
    "\n",
    "from gaussian_operator import GaussianOperator\n",
    "from median_operator import MedianOperator\n",
    "from sobel_operator import SobelOperator\n",
    "\n",
    "from monai.deploy.conditions import CountCondition\n",
    "from monai.deploy.core import Application\n",
    "\n",
    "\n",
    "# Decorator support is not available in this version of the SDK, to be re-introduced later\n",
    "# @resource(cpu=1)\n",
    "class App(Application):\n",
    "    \"\"\"This is a very basic application.\n",
    "\n",
    "    This showcases the MONAI Deploy application framework.\n",
    "    \"\"\"\n",
    "\n",
    "    # App's name. <class name>('App') if not specified.\n",
    "    name = \"simple_imaging_app\"\n",
    "    # App's description. <class docstring> if not specified.\n",
    "    description = \"This is a very simple application.\"\n",
    "    # App's version. <git version tag> or '0.0.0' if not specified.\n",
    "    version = \"0.1.0\"\n",
    "\n",
    "    def compose(self):\n",
    "        \"\"\"This application has three operators.\n",
    "\n",
    "        Each operator has a single input and a single output port.\n",
    "        Each operator performs some kind of image processing function.\n",
    "        \"\"\"\n",
    "        # Use Commandline options over environment variables to init context.\n",
    "        app_context = Application.init_app_context(self.argv)\n",
    "        sample_data_path = Path(app_context.input_path)\n",
    "        output_data_path = Path(app_context.output_path)\n",
    "        logging.info(f\"sample_data_path: {sample_data_path}\")\n",
    "\n",
    "        # Please note that the Application object, self, is passed as the first positional argument\n",
    "        # and the others as kwargs.\n",
    "        # Also note the CountCondition of 1 on the first operator, indicating to the application executor\n",
    "        # to invoke this operator, hence the pipeline, only once.\n",
    "        sobel_op = SobelOperator(self, CountCondition(self, 1), input_path=sample_data_path, name=\"sobel_op\")\n",
    "        median_op = MedianOperator(self, name=\"median_op\")\n",
    "        gaussian_op = GaussianOperator(self, output_folder=output_data_path, name=\"gaussian_op\")\n",
    "        self.add_flow(\n",
    "            sobel_op,\n",
    "            median_op,\n",
    "            {\n",
    "                (\"out1\", \"in1\"),\n",
    "            },\n",
    "        )\n",
    "        self.add_flow(\n",
    "            median_op,\n",
    "            gaussian_op,\n",
    "            {\n",
    "                (\n",
    "                    \"out1\",\n",
    "                    \"in1\",\n",
    "                )\n",
    "            },\n",
    "        )\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    logging.info(f\"Begin {__name__}\")\n",
    "    App().run()\n",
    "    logging.info(f\"End {__name__}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "if __name__ == \"__main__\":\n",
    "    App().run()\n",
    "```\n",
    "\n",
    "The above lines are needed to execute the application code by using `python` interpreter.\n",
    "\n",
    "### \\_\\_main\\_\\_.py\n",
    "\n",
    "\\_\\_main\\_\\_.py is needed for <a href=\"../../developing_with_sdk/packaging_app.html\">MONAI Application Packager</a> to detect the main application code (`app.py`) when the application is executed with the application folder path (e.g., `python simple_imaging_app`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting simple_imaging_app/__main__.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile simple_imaging_app/__main__.py\n",
    "from app import App\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    App().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "app.py\t  gaussian_operator.py\tmedian_operator.py  requirements.txt\n",
      "app.yaml  __main__.py\t\t__pycache__\t    sobel_operator.py\n"
     ]
    }
   ],
   "source": [
    "!ls simple_imaging_app"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, let's execute the app in the command line.\n",
    "\n",
    ":::{note}\n",
    "Since the environment variables have been set and contain the correct paths, it is not necessary to provide the command line options on running the application, though the following demonstrates the use of the options.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[32minfo\u001b[m] [fragment.cpp:705] Loading extensions from configs...\n",
      "[2025-04-22 12:03:12,662] [INFO] (root) - Parsed args: Namespace(log_level='DEBUG', input=PosixPath('/tmp/simple_app'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), model=None, workdir=None, triton_server_netloc=None, argv=['simple_imaging_app', '-i', '/tmp/simple_app', '-o', 'output', '-l', 'DEBUG'])\n",
      "[2025-04-22 12:03:12,666] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=models, workdir=), triton_server_netloc=\n",
      "[2025-04-22 12:03:12,666] [INFO] (root) - sample_data_path: /tmp/simple_app\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:265] Creating context\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2396] Activating Graph...\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2426] Running Graph...\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2428] Waiting for completion...\n",
      "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:191] Scheduling 3 entities\n",
      "Number of times operator sobel_op whose class is defined in sobel_operator called: 1\n",
      "Input from: /tmp/simple_app, whose absolute path: /tmp/simple_app\n",
      "[2025-04-22 12:03:12,705] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9\n",
      "[2025-04-22 12:03:12,706] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445\n",
      "Number of times operator median_op whose class is defined in median_operator called: 1\n",
      "Number of times operator gaussian_op whose class is defined in gaussian_operator called: 1\n",
      "Data type of output: <class 'numpy.ndarray'>, max = 0.35821119421406195\n",
      "Data type of output post conversion: <class 'numpy.ndarray'>, max = 91\n",
      "[2025-04-22 12:03:12,985] [DEBUG] (PIL.Image) - Importing AvifImagePlugin\n",
      "[2025-04-22 12:03:12,986] [DEBUG] (PIL.Image) - Importing BlpImagePlugin\n",
      "[2025-04-22 12:03:12,987] [DEBUG] (PIL.Image) - Importing BmpImagePlugin\n",
      "[2025-04-22 12:03:12,987] [DEBUG] (PIL.Image) - Importing BufrStubImagePlugin\n",
      "[2025-04-22 12:03:12,987] [DEBUG] (PIL.Image) - Importing CurImagePlugin\n",
      "[2025-04-22 12:03:12,987] [DEBUG] (PIL.Image) - Importing DcxImagePlugin\n",
      "[2025-04-22 12:03:12,987] [DEBUG] (PIL.Image) - Importing DdsImagePlugin\n",
      "[2025-04-22 12:03:12,990] [DEBUG] (PIL.Image) - Importing EpsImagePlugin\n",
      "[2025-04-22 12:03:12,991] [DEBUG] (PIL.Image) - Importing FitsImagePlugin\n",
      "[2025-04-22 12:03:12,991] [DEBUG] (PIL.Image) - Importing FliImagePlugin\n",
      "[2025-04-22 12:03:12,991] [DEBUG] (PIL.Image) - Importing FpxImagePlugin\n",
      "[2025-04-22 12:03:12,991] [DEBUG] (PIL.Image) - Image: failed to import FpxImagePlugin: No module named 'olefile'\n",
      "[2025-04-22 12:03:12,991] [DEBUG] (PIL.Image) - Importing FtexImagePlugin\n",
      "[2025-04-22 12:03:12,992] [DEBUG] (PIL.Image) - Importing GbrImagePlugin\n",
      "[2025-04-22 12:03:12,992] [DEBUG] (PIL.Image) - Importing GifImagePlugin\n",
      "[2025-04-22 12:03:12,992] [DEBUG] (PIL.Image) - Importing GribStubImagePlugin\n",
      "[2025-04-22 12:03:12,992] [DEBUG] (PIL.Image) - Importing Hdf5StubImagePlugin\n",
      "[2025-04-22 12:03:12,992] [DEBUG] (PIL.Image) - Importing IcnsImagePlugin\n",
      "[2025-04-22 12:03:12,993] [DEBUG] (PIL.Image) - Importing IcoImagePlugin\n",
      "[2025-04-22 12:03:12,993] [DEBUG] (PIL.Image) - Importing ImImagePlugin\n",
      "[2025-04-22 12:03:12,994] [DEBUG] (PIL.Image) - Importing ImtImagePlugin\n",
      "[2025-04-22 12:03:12,994] [DEBUG] (PIL.Image) - Importing IptcImagePlugin\n",
      "[2025-04-22 12:03:12,994] [DEBUG] (PIL.Image) - Importing JpegImagePlugin\n",
      "[2025-04-22 12:03:12,994] [DEBUG] (PIL.Image) - Importing Jpeg2KImagePlugin\n",
      "[2025-04-22 12:03:12,994] [DEBUG] (PIL.Image) - Importing McIdasImagePlugin\n",
      "[2025-04-22 12:03:12,994] [DEBUG] (PIL.Image) - Importing MicImagePlugin\n",
      "[2025-04-22 12:03:12,995] [DEBUG] (PIL.Image) - Image: failed to import MicImagePlugin: No module named 'olefile'\n",
      "[2025-04-22 12:03:12,995] [DEBUG] (PIL.Image) - Importing MpegImagePlugin\n",
      "[2025-04-22 12:03:12,995] [DEBUG] (PIL.Image) - Importing MpoImagePlugin\n",
      "[2025-04-22 12:03:12,996] [DEBUG] (PIL.Image) - Importing MspImagePlugin\n",
      "[2025-04-22 12:03:12,996] [DEBUG] (PIL.Image) - Importing PalmImagePlugin\n",
      "[2025-04-22 12:03:12,997] [DEBUG] (PIL.Image) - Importing PcdImagePlugin\n",
      "[2025-04-22 12:03:12,997] [DEBUG] (PIL.Image) - Importing PcxImagePlugin\n",
      "[2025-04-22 12:03:12,997] [DEBUG] (PIL.Image) - Importing PdfImagePlugin\n",
      "[2025-04-22 12:03:13,001] [DEBUG] (PIL.Image) - Importing PixarImagePlugin\n",
      "[2025-04-22 12:03:13,002] [DEBUG] (PIL.Image) - Importing PngImagePlugin\n",
      "[2025-04-22 12:03:13,002] [DEBUG] (PIL.Image) - Importing PpmImagePlugin\n",
      "[2025-04-22 12:03:13,002] [DEBUG] (PIL.Image) - Importing PsdImagePlugin\n",
      "[2025-04-22 12:03:13,002] [DEBUG] (PIL.Image) - Importing QoiImagePlugin\n",
      "[2025-04-22 12:03:13,002] [DEBUG] (PIL.Image) - Importing SgiImagePlugin\n",
      "[2025-04-22 12:03:13,002] [DEBUG] (PIL.Image) - Importing SpiderImagePlugin\n",
      "[2025-04-22 12:03:13,002] [DEBUG] (PIL.Image) - Importing SunImagePlugin\n",
      "[2025-04-22 12:03:13,003] [DEBUG] (PIL.Image) - Importing TgaImagePlugin\n",
      "[2025-04-22 12:03:13,003] [DEBUG] (PIL.Image) - Importing TiffImagePlugin\n",
      "[2025-04-22 12:03:13,003] [DEBUG] (PIL.Image) - Importing WebPImagePlugin\n",
      "[2025-04-22 12:03:13,004] [DEBUG] (PIL.Image) - Importing WmfImagePlugin\n",
      "[2025-04-22 12:03:13,004] [DEBUG] (PIL.Image) - Importing XbmImagePlugin\n",
      "[2025-04-22 12:03:13,006] [DEBUG] (PIL.Image) - Importing XpmImagePlugin\n",
      "[2025-04-22 12:03:13,006] [DEBUG] (PIL.Image) - Importing XVThumbImagePlugin\n",
      "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:372] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n",
      "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:401] Scheduler finished.\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2431] Deactivating Graph...\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2439] Graph execution finished.\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:295] Destroying context\n"
     ]
    }
   ],
   "source": [
    "!rm -rf {output_path}\n",
    "!python simple_imaging_app -i {test_input_folder} -o {output_path} -l DEBUG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_895166/3197869135.py:3: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n",
      "  io.imshow(output_image)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7e9183b4e6b0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#output_image_path was set as before, output_image_path = output_path + \"/final_output.png\"\n",
    "output_image = io.imread(output_image_path)\n",
    "io.imshow(output_image)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packaging app"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's package the app with <a href=\"../../developing_with_sdk/packaging_app.html\">MONAI Application Packager</a>.\n",
    "\n",
    "In this version of the App SDK, we need to write out the configuration yaml file as well as the package requirements file, in the application folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting simple_imaging_app/app.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile simple_imaging_app/app.yaml\n",
    "%YAML 1.2\n",
    "---\n",
    "application:\n",
    "  title: MONAI Deploy App Package - Simple Imaging App\n",
    "  version: 1.0\n",
    "  inputFormats: [\"file\"]\n",
    "  outputFormats: [\"file\"]\n",
    "\n",
    "resources:\n",
    "  cpu: 1\n",
    "  gpu: 1\n",
    "  memory: 1Gi\n",
    "  gpuMemory: 1Gi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting simple_imaging_app/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile simple_imaging_app/requirements.txt\n",
    "scikit-image\n",
    "setuptools>=59.5.0 # for pkg_resources\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the CLI package command to build the MONAI Application Package (MAP) container image based on a supported base image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-04-22 12:03:14,884] [INFO] (common) - Downloading CLI manifest file...\n",
      "[2025-04-22 12:03:14,921] [DEBUG] (common) - Validating CLI manifest file...\n",
      "[2025-04-22 12:03:14,922] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app\n",
      "[2025-04-22 12:03:14,922] [INFO] (packager.parameters) - Detected application type: Python Module\n",
      "[2025-04-22 12:03:14,922] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app/app.yaml...\n",
      "[2025-04-22 12:03:14,927] [INFO] (packager) - Generating app.json...\n",
      "[2025-04-22 12:03:14,928] [INFO] (packager) - Generating pkg.json...\n",
      "[2025-04-22 12:03:14,932] [DEBUG] (common) - \n",
      "=============== Begin app.json ===============\n",
      "{\n",
      "    \"apiVersion\": \"1.0.0\",\n",
      "    \"command\": \"[\\\"python3\\\", \\\"/opt/holoscan/app\\\"]\",\n",
      "    \"environment\": {\n",
      "        \"HOLOSCAN_APPLICATION\": \"/opt/holoscan/app\",\n",
      "        \"HOLOSCAN_INPUT_PATH\": \"input/\",\n",
      "        \"HOLOSCAN_OUTPUT_PATH\": \"output/\",\n",
      "        \"HOLOSCAN_WORKDIR\": \"/var/holoscan\",\n",
      "        \"HOLOSCAN_MODEL_PATH\": \"/opt/holoscan/models\",\n",
      "        \"HOLOSCAN_CONFIG_PATH\": \"/var/holoscan/app.yaml\",\n",
      "        \"HOLOSCAN_APP_MANIFEST_PATH\": \"/etc/holoscan/app.json\",\n",
      "        \"HOLOSCAN_PKG_MANIFEST_PATH\": \"/etc/holoscan/pkg.json\",\n",
      "        \"HOLOSCAN_DOCS_PATH\": \"/opt/holoscan/docs\",\n",
      "        \"HOLOSCAN_LOGS_PATH\": \"/var/holoscan/logs\"\n",
      "    },\n",
      "    \"input\": {\n",
      "        \"path\": \"input/\",\n",
      "        \"formats\": null\n",
      "    },\n",
      "    \"liveness\": null,\n",
      "    \"output\": {\n",
      "        \"path\": \"output/\",\n",
      "        \"formats\": null\n",
      "    },\n",
      "    \"readiness\": null,\n",
      "    \"sdk\": \"monai-deploy\",\n",
      "    \"sdkVersion\": \"3.0.0\",\n",
      "    \"timeout\": 0,\n",
      "    \"version\": 1.0,\n",
      "    \"workingDirectory\": \"/var/holoscan\"\n",
      "}\n",
      "================ End app.json ================\n",
      "                 \n",
      "[2025-04-22 12:03:14,933] [DEBUG] (common) - \n",
      "=============== Begin pkg.json ===============\n",
      "{\n",
      "    \"apiVersion\": \"1.0.0\",\n",
      "    \"applicationRoot\": \"/opt/holoscan/app\",\n",
      "    \"modelRoot\": \"/opt/holoscan/models\",\n",
      "    \"models\": {},\n",
      "    \"resources\": {\n",
      "        \"cpu\": 1,\n",
      "        \"gpu\": 1,\n",
      "        \"memory\": \"1Gi\",\n",
      "        \"gpuMemory\": \"1Gi\"\n",
      "    },\n",
      "    \"version\": 1.0,\n",
      "    \"platformConfig\": \"dgpu\"\n",
      "}\n",
      "================ End pkg.json ================\n",
      "                 \n",
      "[2025-04-22 12:03:14,938] [DEBUG] (packager.builder) - \n",
      "========== Begin Build Parameters ==========\n",
      "{'additional_lib_paths': '',\n",
      " 'app_config_file_path': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app/app.yaml'),\n",
      " 'app_dir': PosixPath('/opt/holoscan/app'),\n",
      " 'app_json': '/etc/holoscan/app.json',\n",
      " 'application': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app'),\n",
      " 'application_directory': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app'),\n",
      " 'application_type': 'PythonModule',\n",
      " 'build_cache': PosixPath('/home/mqin/.holoscan_build_cache'),\n",
      " 'cmake_args': '',\n",
      " 'command': '[\"python3\", \"/opt/holoscan/app\"]',\n",
      " 'command_filename': 'simple_imaging_app',\n",
      " 'config_file_path': PosixPath('/var/holoscan/app.yaml'),\n",
      " 'docs_dir': PosixPath('/opt/holoscan/docs'),\n",
      " 'full_input_path': PosixPath('/var/holoscan/input'),\n",
      " 'full_output_path': PosixPath('/var/holoscan/output'),\n",
      " 'gid': 1000,\n",
      " 'holoscan_sdk_version': '3.1.0',\n",
      " 'includes': [],\n",
      " 'input_dir': 'input/',\n",
      " 'lib_dir': PosixPath('/opt/holoscan/lib'),\n",
      " 'logs_dir': PosixPath('/var/holoscan/logs'),\n",
      " 'models_dir': PosixPath('/opt/holoscan/models'),\n",
      " 'monai_deploy_app_sdk_version': '3.0.0',\n",
      " 'no_cache': False,\n",
      " 'output_dir': 'output/',\n",
      " 'pip_packages': None,\n",
      " 'pkg_json': '/etc/holoscan/pkg.json',\n",
      " 'requirements_file_path': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app/requirements.txt'),\n",
      " 'sdk': <SdkType.MonaiDeploy: 'monai-deploy'>,\n",
      " 'sdk_type': 'monai-deploy',\n",
      " 'tarball_output': None,\n",
      " 'timeout': 0,\n",
      " 'title': 'MONAI Deploy App Package - Simple Imaging App',\n",
      " 'uid': 1000,\n",
      " 'username': 'holoscan',\n",
      " 'version': 1.0,\n",
      " 'working_dir': PosixPath('/var/holoscan')}\n",
      "=========== End Build Parameters ===========\n",
      "\n",
      "[2025-04-22 12:03:14,939] [DEBUG] (packager.builder) - \n",
      "========== Begin Platform Parameters ==========\n",
      "{'base_image': 'nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04',\n",
      " 'build_image': None,\n",
      " 'cuda_deb_arch': 'x86_64',\n",
      " 'custom_base_image': False,\n",
      " 'custom_holoscan_sdk': False,\n",
      " 'custom_monai_deploy_sdk': False,\n",
      " 'gpu_type': 'dgpu',\n",
      " 'holoscan_deb_arch': 'amd64',\n",
      " 'holoscan_sdk_file': '3.1.0',\n",
      " 'holoscan_sdk_filename': '3.1.0',\n",
      " 'monai_deploy_sdk_file': None,\n",
      " 'monai_deploy_sdk_filename': None,\n",
      " 'tag': 'simple_imaging_app:1.0',\n",
      " 'target_arch': 'x86_64'}\n",
      "=========== End Platform Parameters ===========\n",
      "\n",
      "[2025-04-22 12:03:14,964] [DEBUG] (packager.builder) - \n",
      "========== Begin Dockerfile ==========\n",
      "\n",
      "ARG GPU_TYPE=dgpu\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "FROM nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04 AS base\n",
      "\n",
      "RUN apt-get update \\\n",
      "    && apt-get install -y --no-install-recommends --no-install-suggests \\\n",
      "        curl \\\n",
      "        jq \\\n",
      "    && rm -rf /var/lib/apt/lists/*\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "# FROM base AS mofed-installer\n",
      "# ARG MOFED_VERSION=23.10-2.1.3.1\n",
      "\n",
      "# # In a container, we only need to install the user space libraries, though the drivers are still\n",
      "# # needed on the host.\n",
      "# # Note: MOFED's installation is not easily portable, so we can't copy the output of this stage\n",
      "# # to our final stage, but must inherit from it. For that reason, we keep track of the build/install\n",
      "# # only dependencies in the `MOFED_DEPS` variable (parsing the output of `--check-deps-only`) to\n",
      "# # remove them in that same layer, to ensure they are not propagated in the final image.\n",
      "# WORKDIR /opt/nvidia/mofed\n",
      "# ARG MOFED_INSTALL_FLAGS=\"--dpdk --with-mft --user-space-only --force --without-fw-update\"\n",
      "# RUN UBUNTU_VERSION=$(cat /etc/lsb-release | grep DISTRIB_RELEASE | cut -d= -f2) \\\n",
      "#     && OFED_PACKAGE=\"MLNX_OFED_LINUX-${MOFED_VERSION}-ubuntu${UBUNTU_VERSION}-$(uname -m)\" \\\n",
      "#     && curl -S -# -o ${OFED_PACKAGE}.tgz -L \\\n",
      "#         https://www.mellanox.com/downloads/ofed/MLNX_OFED-${MOFED_VERSION}/${OFED_PACKAGE}.tgz \\\n",
      "#     && tar xf ${OFED_PACKAGE}.tgz \\\n",
      "#     && MOFED_INSTALLER=$(find . -name mlnxofedinstall -type f -executable -print) \\\n",
      "#     && MOFED_DEPS=$(${MOFED_INSTALLER} ${MOFED_INSTALL_FLAGS} --check-deps-only 2>/dev/null | tail -n1 |  cut -d' ' -f3-) \\\n",
      "#     && apt-get update \\\n",
      "#     && apt-get install --no-install-recommends -y ${MOFED_DEPS} \\\n",
      "#     && ${MOFED_INSTALLER} ${MOFED_INSTALL_FLAGS} \\\n",
      "#     && rm -r * \\\n",
      "#     && apt-get remove -y ${MOFED_DEPS} && apt-get autoremove -y \\\n",
      "#     && rm -rf /var/lib/apt/lists/*\n",
      "\n",
      "FROM base AS release\n",
      "ENV DEBIAN_FRONTEND=noninteractive\n",
      "ENV TERM=xterm-256color\n",
      "\n",
      "ARG GPU_TYPE\n",
      "ARG UNAME\n",
      "ARG UID\n",
      "ARG GID\n",
      "\n",
      "RUN mkdir -p /etc/holoscan/ \\\n",
      "        && mkdir -p /opt/holoscan/ \\\n",
      "        && mkdir -p /var/holoscan \\\n",
      "        && mkdir -p /opt/holoscan/app \\\n",
      "        && mkdir -p /var/holoscan/input \\\n",
      "        && mkdir -p /var/holoscan/output\n",
      "\n",
      "LABEL base=\"nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\"\n",
      "LABEL tag=\"simple_imaging_app:1.0\"\n",
      "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - Simple Imaging App\"\n",
      "LABEL org.opencontainers.image.version=\"1.0\"\n",
      "LABEL org.nvidia.holoscan=\"3.1.0\"\n",
      "\n",
      "LABEL org.monai.deploy.app-sdk=\"3.0.0\"\n",
      "\n",
      "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n",
      "ENV HOLOSCAN_OUTPUT_PATH=/var/holoscan/output\n",
      "ENV HOLOSCAN_WORKDIR=/var/holoscan\n",
      "ENV HOLOSCAN_APPLICATION=/opt/holoscan/app\n",
      "ENV HOLOSCAN_TIMEOUT=0\n",
      "ENV HOLOSCAN_MODEL_PATH=/opt/holoscan/models\n",
      "ENV HOLOSCAN_DOCS_PATH=/opt/holoscan/docs\n",
      "ENV HOLOSCAN_CONFIG_PATH=/var/holoscan/app.yaml\n",
      "ENV HOLOSCAN_APP_MANIFEST_PATH=/etc/holoscan/app.json\n",
      "ENV HOLOSCAN_PKG_MANIFEST_PATH=/etc/holoscan/pkg.json\n",
      "ENV HOLOSCAN_LOGS_PATH=/var/holoscan/logs\n",
      "ENV HOLOSCAN_VERSION=3.1.0\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "# If torch is installed, we can skip installing Python\n",
      "ENV PYTHON_VERSION=3.10.6-1~22.04\n",
      "ENV PYTHON_PIP_VERSION=22.0.2+dfsg-*\n",
      "\n",
      "RUN apt update \\\n",
      "    && apt-get install -y --no-install-recommends --no-install-suggests \\\n",
      "        python3-minimal=${PYTHON_VERSION} \\\n",
      "        libpython3-stdlib=${PYTHON_VERSION} \\\n",
      "        python3=${PYTHON_VERSION} \\\n",
      "        python3-venv=${PYTHON_VERSION} \\\n",
      "        python3-pip=${PYTHON_PIP_VERSION} \\\n",
      "    && rm -rf /var/lib/apt/lists/*\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "RUN groupadd -f -g $GID $UNAME\n",
      "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n",
      "RUN chown -R holoscan /var/holoscan && \\\n",
      "    chown -R holoscan /var/holoscan/input && \\\n",
      "    chown -R holoscan /var/holoscan/output\n",
      "\n",
      "# Set the working directory\n",
      "WORKDIR /var/holoscan\n",
      "\n",
      "# Copy HAP/MAP tool script\n",
      "COPY ./tools /var/holoscan/tools\n",
      "RUN chmod +x /var/holoscan/tools\n",
      "\n",
      "# Set the working directory\n",
      "WORKDIR /var/holoscan\n",
      "\n",
      "USER $UNAME\n",
      "\n",
      "ENV PATH=/home/${UNAME}/.local/bin:/opt/nvidia/holoscan/bin:$PATH\n",
      "ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/${UNAME}/.local/lib/python3.10/site-packages/holoscan/lib\n",
      "\n",
      "COPY ./pip/requirements.txt /tmp/requirements.txt\n",
      "\n",
      "RUN pip install --upgrade pip\n",
      "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n",
      "\n",
      "\n",
      "# Install MONAI Deploy App SDK\n",
      "\n",
      "# Install MONAI Deploy from PyPI org\n",
      "RUN pip install monai-deploy-app-sdk==3.0.0\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "COPY ./map/app.json /etc/holoscan/app.json\n",
      "COPY ./app.config /var/holoscan/app.yaml\n",
      "COPY ./map/pkg.json /etc/holoscan/pkg.json\n",
      "\n",
      "COPY ./app /opt/holoscan/app\n",
      "\n",
      "\n",
      "ENTRYPOINT [\"/var/holoscan/tools\"]\n",
      "=========== End Dockerfile ===========\n",
      "\n",
      "[2025-04-22 12:03:14,964] [INFO] (packager.builder) - \n",
      "===============================================================================\n",
      "Building image for:                 x64-workstation\n",
      "    Architecture:                   linux/amd64\n",
      "    Base Image:                     nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\n",
      "    Build Image:                    N/A\n",
      "    Cache:                          Enabled\n",
      "    Configuration:                  dgpu\n",
      "    Holoscan SDK Package:           3.1.0\n",
      "    MONAI Deploy App SDK Package:   N/A\n",
      "    gRPC Health Probe:              N/A\n",
      "    SDK Version:                    3.1.0\n",
      "    SDK:                            monai-deploy\n",
      "    Tag:                            simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\n",
      "    Included features/dependencies: N/A\n",
      "    \n",
      "[2025-04-22 12:03:15,380] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n",
      "[2025-04-22 12:03:15,380] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\n",
      "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n",
      "\n",
      "#1 [internal] load build definition from Dockerfile\n",
      "#1 transferring dockerfile: 4.53kB done\n",
      "#1 DONE 0.1s\n",
      "\n",
      "#2 [internal] load metadata for nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\n",
      "#2 DONE 0.1s\n",
      "\n",
      "#3 [internal] load .dockerignore\n",
      "#3 transferring context: 1.80kB done\n",
      "#3 DONE 0.1s\n",
      "\n",
      "#4 [internal] load build context\n",
      "#4 DONE 0.0s\n",
      "\n",
      "#5 importing cache manifest from local:2851983977013277839\n",
      "#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n",
      "#5 DONE 0.0s\n",
      "\n",
      "#6 [base 1/2] FROM nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04@sha256:22fc009e5cea0b8b91d94c99fdd419d2366810b5ea835e47b8343bc15800c186\n",
      "#6 resolve nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04@sha256:22fc009e5cea0b8b91d94c99fdd419d2366810b5ea835e47b8343bc15800c186 0.0s done\n",
      "#6 DONE 0.1s\n",
      "\n",
      "#7 importing cache manifest from nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\n",
      "#7 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n",
      "#7 DONE 0.3s\n",
      "\n",
      "#4 [internal] load build context\n",
      "#4 transferring context: 24.82kB 0.0s done\n",
      "#4 DONE 0.1s\n",
      "\n",
      "#8 [release  9/17] WORKDIR /var/holoscan\n",
      "#8 CACHED\n",
      "\n",
      "#9 [release  2/17] RUN apt update     && apt-get install -y --no-install-recommends --no-install-suggests         python3-minimal=3.10.6-1~22.04         libpython3-stdlib=3.10.6-1~22.04         python3=3.10.6-1~22.04         python3-venv=3.10.6-1~22.04         python3-pip=22.0.2+dfsg-*     && rm -rf /var/lib/apt/lists/*\n",
      "#9 CACHED\n",
      "\n",
      "#10 [base 2/2] RUN apt-get update     && apt-get install -y --no-install-recommends --no-install-suggests         curl         jq     && rm -rf /var/lib/apt/lists/*\n",
      "#10 CACHED\n",
      "\n",
      "#11 [release  5/17] RUN chown -R holoscan /var/holoscan &&     chown -R holoscan /var/holoscan/input &&     chown -R holoscan /var/holoscan/output\n",
      "#11 CACHED\n",
      "\n",
      "#12 [release  8/17] RUN chmod +x /var/holoscan/tools\n",
      "#12 CACHED\n",
      "\n",
      "#13 [release 10/17] COPY ./pip/requirements.txt /tmp/requirements.txt\n",
      "#13 CACHED\n",
      "\n",
      "#14 [release  6/17] WORKDIR /var/holoscan\n",
      "#14 CACHED\n",
      "\n",
      "#15 [release  4/17] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n",
      "#15 CACHED\n",
      "\n",
      "#16 [release 11/17] RUN pip install --upgrade pip\n",
      "#16 CACHED\n",
      "\n",
      "#17 [release  1/17] RUN mkdir -p /etc/holoscan/         && mkdir -p /opt/holoscan/         && mkdir -p /var/holoscan         && mkdir -p /opt/holoscan/app         && mkdir -p /var/holoscan/input         && mkdir -p /var/holoscan/output\n",
      "#17 CACHED\n",
      "\n",
      "#18 [release  7/17] COPY ./tools /var/holoscan/tools\n",
      "#18 CACHED\n",
      "\n",
      "#19 [release  3/17] RUN groupadd -f -g 1000 holoscan\n",
      "#19 CACHED\n",
      "\n",
      "#20 [release 12/17] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n",
      "#20 CACHED\n",
      "\n",
      "#21 [release 13/17] RUN pip install monai-deploy-app-sdk==3.0.0\n",
      "#21 0.756 Defaulting to user installation because normal site-packages is not writeable\n",
      "#21 0.918 ERROR: Could not find a version that satisfies the requirement monai-deploy-app-sdk==3.0.0 (from versions: 0.1.0a2, 0.1.0rc1, 0.1.0rc2, 0.1.0rc3, 0.1.0, 0.1.1rc1, 0.1.1, 0.2.0, 0.2.1, 0.3.0, 0.4.0, 0.5.0, 0.5.1, 0.6.0, 1.0.0, 2.0.0)\n",
      "#21 1.066 ERROR: No matching distribution found for monai-deploy-app-sdk==3.0.0\n",
      "#21 ERROR: process \"/bin/sh -c pip install monai-deploy-app-sdk==3.0.0\" did not complete successfully: exit code: 1\n",
      "------\n",
      " > [release 13/17] RUN pip install monai-deploy-app-sdk==3.0.0:\n",
      "0.756 Defaulting to user installation because normal site-packages is not writeable\n",
      "0.918 ERROR: Could not find a version that satisfies the requirement monai-deploy-app-sdk==3.0.0 (from versions: 0.1.0a2, 0.1.0rc1, 0.1.0rc2, 0.1.0rc3, 0.1.0, 0.1.1rc1, 0.1.1, 0.2.0, 0.2.1, 0.3.0, 0.4.0, 0.5.0, 0.5.1, 0.6.0, 1.0.0, 2.0.0)\n",
      "1.066 ERROR: No matching distribution found for monai-deploy-app-sdk==3.0.0\n",
      "------\n",
      "Dockerfile:137\n",
      "--------------------\n",
      " 135 |     \n",
      " 136 |     # Install MONAI Deploy from PyPI org\n",
      " 137 | >>> RUN pip install monai-deploy-app-sdk==3.0.0\n",
      " 138 |     \n",
      " 139 |     \n",
      "--------------------\n",
      "ERROR: failed to solve: process \"/bin/sh -c pip install monai-deploy-app-sdk==3.0.0\" did not complete successfully: exit code: 1\n",
      "[2025-04-22 12:03:17,946] [INFO] (packager) - Build Summary:\n",
      "\n",
      "Platform: x64-workstation/dgpu\n",
      "    Status: Failure\n",
      "    Error:  Error building image: see Docker output for additional details.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "tag_prefix = \"simple_imaging_app\"\n",
    "\n",
    "!monai-deploy package simple_imaging_app -c simple_imaging_app/app.yaml -t {tag_prefix}:1.0 --platform x86_64 -l DEBUG"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "Building a MONAI Application Package (Docker image) can take time. Use `-l DEBUG` option if you want to see the progress.\n",
    "\n",
    ":::\n",
    "\n",
    "We can see that the MAP Docker image is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simple_imaging_app-x64-workstation-dgpu-linux-amd64                           1.0                            ffe41584f515   2 hours ago     3.49GB\n"
     ]
    }
   ],
   "source": [
    "!docker image ls | grep {tag_prefix}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can choose to display and inspect the MAP manifests by running the container with the `show` command.\n",
    "Furthermore, we can also extract the manifests and other contents in the MAP by using the `extract` command while mapping specific folder to the host's (we know that our MAP is compliant and supports these commands).\n",
    "\n",
    ":::{note}\n",
    "The host folder for storing the extracted content must first be created by the user, and if it has been created by Docker on running the container, the folder needs to be deleted and re-created.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display manifests and extract MAP contents to the host folder, ./export\n",
      "\n",
      "============================== app.json ==============================\n",
      "{\n",
      "  \"apiVersion\": \"1.0.0\",\n",
      "  \"command\": \"[\\\"python3\\\", \\\"/opt/holoscan/app\\\"]\",\n",
      "  \"environment\": {\n",
      "    \"HOLOSCAN_APPLICATION\": \"/opt/holoscan/app\",\n",
      "    \"HOLOSCAN_INPUT_PATH\": \"input/\",\n",
      "    \"HOLOSCAN_OUTPUT_PATH\": \"output/\",\n",
      "    \"HOLOSCAN_WORKDIR\": \"/var/holoscan\",\n",
      "    \"HOLOSCAN_MODEL_PATH\": \"/opt/holoscan/models\",\n",
      "    \"HOLOSCAN_CONFIG_PATH\": \"/var/holoscan/app.yaml\",\n",
      "    \"HOLOSCAN_APP_MANIFEST_PATH\": \"/etc/holoscan/app.json\",\n",
      "    \"HOLOSCAN_PKG_MANIFEST_PATH\": \"/etc/holoscan/pkg.json\",\n",
      "    \"HOLOSCAN_DOCS_PATH\": \"/opt/holoscan/docs\",\n",
      "    \"HOLOSCAN_LOGS_PATH\": \"/var/holoscan/logs\"\n",
      "  },\n",
      "  \"input\": {\n",
      "    \"path\": \"input/\",\n",
      "    \"formats\": null\n",
      "  },\n",
      "  \"liveness\": null,\n",
      "  \"output\": {\n",
      "    \"path\": \"output/\",\n",
      "    \"formats\": null\n",
      "  },\n",
      "  \"readiness\": null,\n",
      "  \"sdk\": \"monai-deploy\",\n",
      "  \"sdkVersion\": \"0.5.1\",\n",
      "  \"timeout\": 0,\n",
      "  \"version\": 1,\n",
      "  \"workingDirectory\": \"/var/holoscan\"\n",
      "}\n",
      "\n",
      "============================== pkg.json ==============================\n",
      "{\n",
      "  \"apiVersion\": \"1.0.0\",\n",
      "  \"applicationRoot\": \"/opt/holoscan/app\",\n",
      "  \"modelRoot\": \"/opt/holoscan/models\",\n",
      "  \"models\": {},\n",
      "  \"resources\": {\n",
      "    \"cpu\": 1,\n",
      "    \"gpu\": 1,\n",
      "    \"memory\": \"1Gi\",\n",
      "    \"gpuMemory\": \"1Gi\"\n",
      "  },\n",
      "  \"version\": 1,\n",
      "  \"platformConfig\": \"dgpu\"\n",
      "}\n",
      "\n",
      "2025-04-22 19:03:20 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app\n",
      "\n",
      "2025-04-22 19:03:20 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json\n",
      "2025-04-22 19:03:20 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json\n",
      "2025-04-22 19:03:20 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml\n",
      "\n",
      "2025-04-22 19:03:20 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models\n",
      "2025-04-22 19:03:20 [INFO] '/opt/holoscan/models' cannot be found.\n",
      "\n",
      "2025-04-22 19:03:20 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs\n",
      "2025-04-22 19:03:20 [INFO] '/opt/holoscan/docs/' cannot be found.\n",
      "\n",
      "app  config\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!echo \"Display manifests and extract MAP contents to the host folder, ./export\"\n",
    "!docker run --rm {tag_prefix}-x64-workstation-dgpu-linux-amd64:1.0 show\n",
    "!rm -rf `pwd`/export && mkdir -p `pwd`/export\n",
    "!docker run --rm -v `pwd`/export/:/var/run/holoscan/export/ {tag_prefix}-x64-workstation-dgpu-linux-amd64:1.0 extract\n",
    "!ls `pwd`/export"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing packaged app locally\n",
    "\n",
    "The packaged app can be run locally through <a href=\"../../developing_with_sdk/executing_packaged_app_locally.html\">MONAI Application Runner</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-04-22 12:03:21,611] [INFO] (runner) - Checking dependencies...\n",
      "[2025-04-22 12:03:21,611] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n",
      "\n",
      "[2025-04-22 12:03:21,611] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n",
      "\n",
      "[2025-04-22 12:03:21,611] [INFO] (runner) - --> Verifying if \"simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n",
      "\n",
      "[2025-04-22 12:03:21,668] [INFO] (runner) - Reading HAP/MAP manifest...\n",
      "Successfully copied 2.56kB to /tmp/tmp743z7364/app.json\n",
      "Successfully copied 2.05kB to /tmp/tmp743z7364/pkg.json\n",
      "a1173c75c310b2aad23825b7ac8ec738134ee182f68590011dc0c8f3d3fb2853\n",
      "[2025-04-22 12:03:21,918] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n",
      "\n",
      "[2025-04-22 12:03:21,918] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n",
      "\n",
      "[2025-04-22 12:03:22,256] [INFO] (common) - Launching container (652dede999f5) using image 'simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0'...\n",
      "    container name:      frosty_hofstadter\n",
      "    host name:           mingq-dt\n",
      "    network:             host\n",
      "    user:                1000:1000\n",
      "    ulimits:             memlock=-1:-1, stack=67108864:67108864\n",
      "    cap_add:             CAP_SYS_PTRACE\n",
      "    ipc mode:            host\n",
      "    shared memory size:  67108864\n",
      "    devices:             \n",
      "    group_add:           44\n",
      "2025-04-22 19:03:22 [INFO] Launching application python3 /opt/holoscan/app ...\n",
      "\n",
      "[info] [fragment.cpp:705] Loading extensions from configs...\n",
      "\n",
      "[info] [gxf_executor.cpp:265] Creating context\n",
      "\n",
      "[2025-04-22 19:03:23,790] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, triton_server_netloc=None, argv=['/opt/holoscan/app'])\n",
      "\n",
      "[2025-04-22 19:03:23,790] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan), triton_server_netloc=\n",
      "\n",
      "[2025-04-22 19:03:23,790] [INFO] (root) - sample_data_path: /var/holoscan/input\n",
      "\n",
      "[info] [gxf_executor.cpp:2396] Activating Graph...\n",
      "\n",
      "[info] [gxf_executor.cpp:2426] Running Graph...\n",
      "\n",
      "[info] [gxf_executor.cpp:2428] Waiting for completion...\n",
      "\n",
      "[info] [greedy_scheduler.cpp:191] Scheduling 3 entities\n",
      "\n",
      "[info] [greedy_scheduler.cpp:372] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n",
      "\n",
      "[info] [greedy_scheduler.cpp:401] Scheduler finished.\n",
      "\n",
      "[info] [gxf_executor.cpp:2431] Deactivating Graph...\n",
      "\n",
      "[info] [gxf_executor.cpp:2439] Graph execution finished.\n",
      "\n",
      "[info] [gxf_executor.cpp:295] Destroying context\n",
      "\n",
      "Number of times operator sobel_op whose class is defined in sobel_operator called: 1\n",
      "\n",
      "Input from: /var/holoscan/input, whose absolute path: /var/holoscan/input\n",
      "\n",
      "Number of times operator median_op whose class is defined in median_operator called: 1\n",
      "\n",
      "Number of times operator gaussian_op whose class is defined in gaussian_operator called: 1\n",
      "\n",
      "Data type of output: <class 'numpy.ndarray'>, max = 0.35821119421406195\n",
      "\n",
      "Data type of output post conversion: <class 'numpy.ndarray'>, max = 91\n",
      "\n",
      "[2025-04-22 12:03:24,743] [INFO] (common) - Container 'frosty_hofstadter'(652dede999f5) exited.\n"
     ]
    }
   ],
   "source": [
    "# Clear the output folder and run the MAP container. The input is expected to be a folder\n",
    "!rm -rf {output_path}\n",
    "!monai-deploy run -i {test_input_folder} -o {output_path} {tag_prefix}-x64-workstation-dgpu-linux-amd64:1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_895166/3197869135.py:3: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n",
      "  io.imshow(output_image)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7e91839d84c0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#output_image_path was set as before, output_image_path = output_path + \"/final_output.png\"\n",
    "output_image = io.imread(output_image_path)\n",
    "io.imshow(output_image)"
   ]
  }
 ],
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